Advanced Control Strategies for Energy-Efficient Electric Vehicle Cabin Air Conditioning Systems: A Review

Engineering notes

Key topics: autonomous driving, bev, reinforcement learning, prediction, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Energy efficiency is a critical challenge in heating, ventilation, and air conditioning (HVAC) systems in battery electric vehicles (BEVs), as they are among the main auxiliary systems directly affecting driving range. This review examines control-oriented strategies for EV cabin thermal management, focusing on how advanced control can improve energy utilization while maintaining thermal comfort. Specifically, the review examines predictive control methods, optimization-based strategies, and data-driven learning approaches applied to HVAC systems, with particular emphasis on model predictive control, dynamic programming, and reinforcement learning frameworks. The literature shows that advanced controllers can reduce HVAC energy consumption while maintaining thermal comfort; however, most existing studies still focus on whole-cabin air regulation. In contrast, localized actuators, including seat heaters, radiant panels, infrared heaters, and targeted airflow systems, are rarely optimized or incorporated as explicit manipulated variables in control frameworks. This review identifies the lack of coordinated local–global actuator optimization and control as a major research gap. Future EV cabin thermal management should therefore prioritize human-centric, prediction-aware, and safety-constrained control frameworks that jointly optimize global HVAC operation and localized comfort actuation.

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